Comprehensive Analysis: Monitoring Apache Kafka with Grafana, JMX Exporter, and Prometheus

In the big data era, monitoring and analyzing large amounts of heterogeneous data efficiently has become a major challenge. Monitoring and virtualization tools can significantly influence the performance and behavior of real-time open-source frameworks. The paper provides an analysis of Kafka’s performance by measuring producer and consumer latency and throughput using an integrated monitoring architecture including JMX Exporter, Prometheus, and Grafana. Confirming the experimental results, producer latency can be significantly decreased by up to 30% by increasing the number of partitions, whereas consumer delays may slightly increase because of partition overhead. The throughput investigation indicates that Kafka can achieve balanced producer and consumer throughput by processing a 4.6 MB dataset more effectively with more partitions. The paper offers significant results for improving real-time data processing by demonstrating the scalability and dependability of Kafka's performance when monitored using the proposed architecture.